Search media content based upon tempo

ABSTRACT

A media system includes: a media-playback device including: a media-output device that plays media content items; and a tempo control engine to: select media content based upon suitability for a repetitive-motion activity; place each media content item from the media content into two or more pools of media content, with a first pool including media content which the user has previously indicated as being relevant, and a second pool including media content that the user has not previously indicated as being relevant; and sort the media content in each of the first pool and the second pool based upon tempo.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Ser. No. 62/347,651, filed onJun. 9, 2016, entitled SEARCH MEDIA CONTENT BASED UPON TEMPO, thedisclosure of which is hereby incorporated by reference in its entirety.

BACKGROUND

Running, as well as many other recreation or fitness activities, includerepetitive motions. For example, running and walking involve repetitivesteps, biking involves repetitive rotational movements, rowing involvesrepetitive strokes, and swimming involves repetitive strokes and kicks.There are of course many other recreation and fitness activities thatalso include various repetitive motions. These repetitive motionactivities may be performed in place (e.g., using a treadmill,stationary bike, rowing machine, swimming machine, etc.) or in motion(e.g., on roads, trails, or tracks or in a pool or body of water, etc.).Cadence refers to the frequency of these repetitive motions and is oftenmeasured in terms of motions per minute (e.g., steps per minute,rotations per minute, strokes per minute, or kicks per minute).

Many people enjoy consuming media content, such as listening to audiocontent or watching video content, while running or engaging in otherrepetitive-motion activities. Examples of audio content include songs,albums, podcasts, audiobooks, etc. Examples of video content includemovies, music videos, television episodes, etc. Using a mobile phone orother media-playback device a person can access large catalogs of mediacontent. For example, a user can access an almost limitless catalog ofmedia content through various free and subscription-based streamingservices. Additionally, a user can store a large catalog of mediacontent on his or her mobile device.

This nearly limitless access to media content introduces new challengesfor users. For example, it may be difficult to find or select the rightmedia content that complements a particular moment during a run or otherrepetitive-motion activity.

SUMMARY

In general terms, this disclosure is directed to a system for searchingfor media content of a desired tempo. Various aspects are described inthis disclosure, which include, but are not limited to, the followingaspects.

In one aspect a media system includes: a media-playback deviceincluding: a media-output device that plays media content items; and atempo control engine configured to: select media content based uponsuitability for a repetitive-motion activity; place each media contentitem from the media content into two or more pools of media content,with a first pool including media content which the user has previouslyindicated as being relevant, and a second pool including media contentthat the user has not previously indicated as being relevant; and sortthe media content in each of the first pool and the second pool basedupon tempo.

In another aspect, a media system includes: a media-playback deviceincluding a media-output device that plays media content items, and atempo control engine configured to receive a selection of a desiredtempo; and a server configured to: select media content based uponsuitability for a repetitive-motion activity; place each media contentitem from the media content into two or more pools of media content,with a first pool including media content which the user has previouslyindicated as being relevant, and a second pool including media contentthat the user has not previously indicated as being relevant; and sortthe media content in each of the first pool and the second pool basedupon tempo.

In yet another aspect, a method for selecting media content based upontempo includes: selecting media content based upon suitability for arepetitive-motion activity; placing each media content item from themedia content into two or more pools of media content, with a first poolincluding media content which the user has previously indicated as beingrelevant, and a second pool including media content that the user hasnot previously indicated as being relevant; and sorting the mediacontent in each of the first pool and the second pool based upon tempo.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system for tempo searching and mediacontent selection.

FIG. 2 is a schematic illustration of the example system of FIG. 1.

FIG. 3 is a schematic block diagram of the repetitive-motion activitycontent identification engine of FIG. 2.

FIG. 4 illustrates an example method of identifying media content forplayback during a repetitive-motion activity performed by someembodiments of the repetitive-motion activity content identificationengine of FIG. 2.

FIG. 5 illustrates an example method of acquiring a list of positiveexamples of runnable media content items that is performed by someembodiments of the playlist analysis engine of FIG. 2.

FIG. 6 illustrates an example method of building a runnability modelbased on positive examples of runnable media content items that isperformed by some embodiments of the model building engine of FIG. 2.

FIG. 7 illustrates an example method of evaluating media content itemsusing a runnability model that is performed by some embodiments of thecontent evaluation engine of FIG. 2.

FIG. 8 illustrates an example method of filtering a media content itemfor runnability that is performed by some embodiments of the contentevaluation engine of FIG. 2.

FIG. 9 illustrates an example method of analyzing the playback of mediacontent items during running that is performed by some embodiments ofthe content playback analysis engine of FIG. 2.

FIG. 10 illustrates an example method of selecting media content basedupon tempo by some embodiments of the media-delivery system of FIG. 2.

FIG. 11 illustrates an example method for analyzing the tempo of mediacontent per the method shown in FIG. 10.

FIG. 12 illustrates an example method of filtering media content basedupon criteria per the method shown in FIG. 11.

FIG. 13 illustrates another example method of filtering media contentbased upon criteria per the method shown in FIG. 12.

FIG. 14 shows an example tempo-based content playback screen displayedby some embodiments of the user interface of FIG. 2.

FIG. 15 shows a schematic illustration of an example corpus of mediacontent.

FIG. 16 shows another schematic illustration of an example corpus ofmedia content.

FIG. 17 shows an example method for filtering media content from poolsinto tempo buckets.

DETAILED DESCRIPTION

Various embodiments will be described in detail with reference to thedrawings, wherein like reference numerals represent like parts andassemblies throughout the several views. Reference to variousembodiments does not limit the scope of the claims attached hereto.Additionally, any examples set forth in this specification are notintended to be limiting and merely set forth some of the many possibleembodiments for the appended claims.

Users of media-playback devices often consume media content whileengaging in various activities, including repetitive motion activities.As noted above, examples of repetitive-motion activities may includeswimming, biking, running, rowing, and other activities. Consuming mediacontent may include one or more of listening to audio content, watchingvideo content, or consuming other types of media content. For ease ofexplanation, the embodiments described in this application are presentedusing specific examples. For example, audio content (and in particularmusic) is described as an example of one form of media consumption. Asanother example, running is described as one example of arepetitive-motion activity. However, it should be understood that thesame concepts are equally applicable to other forms of media consumptionand to other forms of repetitive-motion activities, and at least someembodiments include other forms of media consumption and/or other formsof repetitive-motion activities.

The users may desire that the media content fits well with theparticular repetitive-motion activity. For example, a user who isrunning may desire to listen to music with a beat that corresponds tothe user's cadence. Beneficially, by matching the beat of the music tothe cadence, the user's performance or enjoyment of therepetitive-motion activity may be enhanced. This desire cannot be metwith traditional media-playback devices and media-delivery systems.

FIG. 1 illustrates an example system 100 for cadence determination andmedia content selection. The example system 100 includes amedia-playback device 102 and a media-delivery system 104. The system100 communicates across a network 106. Also shown, is a user U who isrunning. The user U's upcoming steps S are shown as well. A steprepresents a single strike of the runner's foot upon the ground.

The media-playback device 102 operates to play media content items toproduce media output 110. In some embodiments, the media content itemsare provided by the media-delivery system 104 and transmitted to themedia-playback device 102 using the network 106. A media content item isan item of media content, including audio, video, or other types ofmedia content, which may be stored in any format suitable for storingmedia content. Non-limiting examples of media content items includesongs, albums, music videos, movies, television episodes, podcasts,other types of audio or video content, and portions or combinationsthereof.

The media-playback device 102 plays media content for the user based onthe user's cadence. In the example shown, the media output 110 includesmusic with a tempo that corresponds to the user's cadence. The tempo (orrhythm) of music refers to the frequency of the beat and is typicallymeasured in beats per minute (BPM). The beat is the basic unit of rhythmin a musical composition (as determined by the time signature of themusic). Accordingly, in the example shown, the user U's steps occur atthe same frequency as the beat of the music.

For example, if the user U is running at a cadence of 180 steps perminute, the media-playback device 102 may play a media content itemhaving a tempo equal to or approximately equal to 180 BPM. In otherembodiments, the media-playback device 102 plays a media content itemhaving a tempo equal to or approximately equal to the result of dividingthe cadence by an integer such as a tempo that is equal to orapproximately equal to one-half (e.g., 90 BPM when the user is runningat a cadence of 180 steps per minute), one-fourth, or one-eighth of thecadence. Alternatively, the media-playback device 102 plays a mediacontent item having a tempo that is equal to or approximately equal toan integer multiple (e.g., 2×, 4×, etc.) of the cadence. Further, insome embodiments, the media-playback device 102 operates to playmultiple media content items including one or more media content itemshaving a tempo equal to or approximately equal to the cadence and one ormore media content items have a tempo equal or approximately equal tothe result of multiplying or dividing the cadence by an integer. Variousother combinations are possible as well.

In some embodiments, the media-playback device 102 operates to playmusic having a tempo that is within a predetermined range of a targettempo. In at least some embodiments, the predetermined range is plus orminus 2.5 BPM. For example, if the user U is running at a cadence of 180steps per minute, the media-playback device 102 operates to play musichaving a tempo of 177.5-182.5 BPM. Alternatively, in other embodiments,the predetermined range is itself in a range from 1 BPM to 10 BPM.

Further, in some embodiments, the media-playback device 102 operates toplay music having a tempo equal to or approximately equal to a user U'scadence after it is rounded. For example, the cadence may be rounded tothe nearest multiple of 2.5, 5, or 10 and then the media-playback device102 plays music having a tempo equal to or approximately equal to therounded cadence. In yet other embodiments, the media-playback device 102uses the cadence to select a predetermined tempo range of music forplayback. For example, if the user U's cadence is 181 steps per minute,the media-playback device 102 may operate to play music from apredetermined tempo range of 180-184.9 BPM; while if the user U'scadence is 178 steps per minute, the media-playback device 102 mayoperate to play music from a predetermined tempo range of 175-179.9 BPM.

FIG. 2 is a schematic illustration of an example system 100 for cadencedetermination and media content selection. In FIG. 2, the media-playbackdevice 102, the media-delivery system 104, and the network 106 areshown. Also shown are the user U and a satellite S.

As noted above, the media-playback device 102 operates to play mediacontent items. In some embodiments, the media-playback device 102operates to play media content items that are provided (e.g., streamed,transmitted, etc.) by a system external to the media-playback devicesuch as the media-delivery system 104, another system, or a peer device.Alternatively, in some embodiments, the media-playback device 102operates to play media content items stored locally on themedia-playback device 102. Further, in at least some embodiments, themedia-playback device 102 operates to play media content items that arestored locally as well as media content items provided by other systems.

In some embodiments, the media-playback device 102 is a computingdevice, handheld entertainment device, smartphone, tablet, watch,wearable device, or any other type of device capable of playing mediacontent. In yet other embodiments, the media-playback device 102 is alaptop computer, desktop computer, television, gaming console, set-topbox, network appliance, blue-ray or DVD player, media player, stereo, orradio.

In at least some embodiments, the media-playback device 102 includes alocation-determining device 150, a touch screen 152, a processing device154, a memory device 156, a content output device 158, acadence-acquiring device 160, and a network access device 162. Otherembodiments may include additional, different, or fewer components. Forexample, some embodiments may include a recording device such as amicrophone or camera that operates to record audio or video content. Asanother example, some embodiments do not include one or more of thelocation-determining device 150 and the touch screen 152.

The location-determining device 150 is a device that determines thelocation of the media-playback device 102. In some embodiments, thelocation-determining device 150 uses one or more of the followingtechnologies: Global Positioning System (GPS) technology which mayreceive GPS signals 170 from satellites S, cellular triangulationtechnology, network-based location identification technology, WI-FI®positioning systems technology, and combinations thereof.

The touch screen 152 operates to receive an input 172 from a selector(e.g., a finger, stylus etc.) controlled by the user U. In someembodiments, the touch screen 152 operates as both a display device anda user input device. In some embodiments, the touch screen 152 detectsinputs based on one or both of touches and near-touches. In someembodiments, the touch screen 152 displays a user interface 164 forinteracting with the media-playback device 102. As noted above, someembodiments do not include a touch screen 152. Some embodiments includea display device and one or more separate user interface devices.Further, some embodiments do not include a display device.

In some embodiments, the processing device 154 comprises one or morecentral processing units (CPU). In other embodiments, the processingdevice 154 additionally or alternatively includes one or more digitalsignal processors, field-programmable gate arrays, or other electroniccircuits.

The memory device 156 operates to store data and instructions. In someembodiments, the memory device 156 stores instructions for amedia-playback engine 166 that includes a tempo search control engine168. In some embodiments, the media-playback engine 166 operates toplayback media content and the tempo search control engine 168 operatesto select media content for playback based on various conditions, suchas tempo. Additional details regarding the tempo search control engine168 are provided below.

The memory device 156 typically includes at least some form ofcomputer-readable media. Computer readable media includes any availablemedia that can be accessed by the media-playback device 102. By way ofexample, computer-readable media include computer readable storage mediaand computer readable communication media.

Computer readable storage media includes volatile and nonvolatile,removable and non-removable media implemented in any device configuredto store information such as computer readable instructions, datastructures, program modules, or other data. Computer readable storagemedia includes, but is not limited to, random access memory, read onlymemory, electrically erasable programmable read only memory, flashmemory and other memory technology, compact disc read only memory, blueray discs, digital versatile discs or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium that can be used to store thedesired information and that can be accessed by the media-playbackdevice 102. In some embodiments, computer readable storage media isnon-transitory computer readable storage media.

Computer readable communication media typically embodies computerreadable instructions, data structures, program modules or other data ina modulated data signal such as a carrier wave or other transportmechanism and includes any information delivery media. The term“modulated data signal” refers to a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, computer readable communication mediaincludes wired media such as a wired network or direct-wired connection,and wireless media such as acoustic, radio frequency, infrared, andother wireless media. Combinations of any of the above are also includedwithin the scope of computer readable media.

The content output device 158 operates to output media content. In someembodiments, the content output device 158 generates media output 110for the user U. Examples of the content output device 158 include aspeaker, an audio output jack, a BLUETOOTH® transmitter, a displaypanel, and a video output jack. Other embodiments are possible as well.For example, the content output device 158 may transmit a signal throughthe audio output jack or BLUETOOTH® transmitter that can be used toreproduce an audio signal by a connected or paired device such asheadphones or a speaker.

The cadence-acquiring device 160 operates to acquire a cadenceassociated with the user U. In at least some embodiments, thecadence-acquiring device 160 operates to determine cadence directly andincludes one or more accelerometers or other motion-detectingtechnologies. Alternatively, the cadence-acquiring device 160 operatesto receive data representing a cadence associated with the user U. Forexample, in some embodiments, the cadence-acquiring device 160 operatesto receive data from a watch, bracelet, foot pod, chest strap, shoeinsert, anklet, smart sock, bicycle computer, exercise equipment (e.g.,treadmill, rowing machine, stationary cycle), or other device fordetermining or measuring cadence. Further, in some embodiments, thecadence-acquiring device 160 operates to receive a cadence value inputby the user U or another person.

The network access device 162 operates to communicate with othercomputing devices over one or more networks, such as the network 106.Examples of the network access device include wired network interfacesand wireless network interfaces. Wireless network interfaces includesinfrared, BLUETOOTH® wireless technology, 802.11a/b/g/n/ac, and cellularor other radio frequency interfaces in at least some possibleembodiments.

The network 106 is an electronic communication network that facilitatescommunication between the media-playback device 102 and themedia-delivery system 104. An electronic communication network includesa set of computing devices and links between the computing devices. Thecomputing devices in the network use the links to enable communicationamong the computing devices in the network. The network 106 can includerouters, switches, mobile access points, bridges, hubs, intrusiondetection devices, storage devices, standalone server devices, bladeserver devices, sensors, desktop computers, firewall devices, laptopcomputers, handheld computers, mobile telephones, and other types ofcomputing devices.

In various embodiments, the network 106 includes various types of links.For example, the network 106 can include wired and/or wireless links,including BLUETOOTH®, ultra-wideband (UWB), 802.11, ZIGBEE®, cellular,and other types of wireless links. Furthermore, in various embodiments,the network 106 is implemented at various scales. For example, thenetwork 106 can be implemented as one or more local area networks(LANs), metropolitan area networks, subnets, wide area networks (such asthe Internet), or can be implemented at another scale. Further, in someembodiments, the network 106 includes multiple networks, which may be ofthe same type or of multiple different types.

The media-delivery system 104 comprises one or more computing devicesand operates to provide media content items to the media-playbackdevices 102 and, in some embodiments, other media-playback devices aswell. The media-delivery system 104 includes a media server 180 and arepetitive-motion activity server 182. In at least some embodiments, themedia server 180 and the repetitive-motion activity server 182 areprovided by separate computing devices. In other embodiments, the mediaserver 180 and the repetitive-motion activity server 182 are provided bythe same computing devices. Further, in some embodiments, one or both ofthe media server 180 and the repetitive-motion activity server 182 areprovided by multiple computing devices. For example, the media server180 and the repetitive-motion activity server 182 may be provided bymultiple redundant servers located in multiple geographic locations.

The media server 180 operates to transmit stream media 218 tomedia-playback devices such as the media-playback device 102. In someembodiments, the media server 180 includes a media server application184, a processing device 186, a memory device 188, and a network accessdevice 190. The processing device 186, memory device 188, and networkaccess device 190 may be similar to the processing device 154, memorydevice 156, and network access device 162 respectively, which have eachbeen previously described.

In some embodiments, the media server application 184 operates to streammusic or other audio, video, or other forms of media content. The mediaserver application 184 includes a media stream service 194, a media datastore 196, and a media application interface 198. The media streamservice 194 operates to buffer media content such as media content items206, 208, and 210, for streaming to one or more streams 200, 202, and204.

The media application interface 198 can receive requests or othercommunication from media-playback devices or other systems, to retrievemedia content items from the media server 180. For example, in FIG. 2,the media application interface 198 receives communication 234 from themedia-playback engine 166.

In some embodiments, the media data store 196 stores media content items212, media content metadata 214, and playlists 216. The media data store196 may comprise one or more databases and file systems. Otherembodiments are possible as well. As noted above, the media contentitems 212 may be audio, video, or any other type of media content, whichmay be stored in any format for storing media content.

The media content metadata 214 operates to provide various informationassociated with the media content items 212. In some embodiments, themedia content metadata 214 includes one or more of title, artist name,album name, length, genre, mood, era, etc. The playlists 216 operate toidentify one or more of the media content items 212 and. In someembodiments, the playlists 216 identify a group of the media contentitems 212 in a particular order. In other embodiments, the playlists 216merely identify a group of the media content items 212 withoutspecifying a particular order. Some, but not necessarily all, of themedia content items 212 included in a particular one of the playlists216 are associated with a common characteristic such as a common genre,mood, or era.

The repetitive-motion activity server 182 operates to providerepetitive-motion activity-specific information about media contentitems to media-playback devices. In some embodiments, therepetitive-motion activity server 182 includes a repetitive-motionactivity server application 220, a processing device 222, a memorydevice 224, and a network access device 226. The processing device 222,memory device 224, and network access device 226 may be similar to theprocessing device 154, memory device 156, and network access device 162respectively, which have each been previously described.

In some embodiments, repetitive-motion activity server application 220operates to transmit information about the suitability of one or moremedia content items for playback during a particular repetitive-motionactivity. The repetitive-motion activity server application 220 includesa repetitive-motion activity interface 228 and a repetitive-motionactivity media metadata store 230.

In some embodiments, the repetitive-motion activity server application220 may provide a list of media content items at a particular tempo to amedia-playback device in response to a request that includes aparticular cadence value. Further, in some embodiments, the mediacontent items included in the returned list will be particularlyrelevant for the repetitive motion activity in which the user is engaged(for example, if the user is running, the returned list of media contentitems may include only media content items that have been identified asbeing highly runnable).

The repetitive-motion activity interface 228 operates to receiverequests or other communication from media-playback devices or othersystems to retrieve information about media content items from therepetitive-motion activity server 182. For example, in FIG. 2, therepetitive-motion activity interface 228 receives communication 236 fromthe media-playback engine 166.

In some embodiments, the repetitive-motion activity media metadata store230 stores repetitive-motion activity media metadata 232. Therepetitive-motion activity media metadata store 230 may comprise one ormore databases and file systems. Other embodiments are possible as well.

The repetitive-motion activity media metadata 232 operates to providevarious information associated with media content items, such as themedia content items 212. In some embodiments, the repetitive-motionactivity media metadata 232 provides information that may be useful forselecting media content items for playback during a repetitive-motionactivity. For example, in some embodiments, the repetitive-motionactivity media metadata 232 stores runnability scores for media contentitems that correspond to the suitability of particular media contentitems for playback during running. As another example, in someembodiments, the repetitive-motion activity media metadata 232 storestimestamps (e.g., start and end points) that identify portions of amedia content items that are particularly well-suited for playbackduring running (or another repetitive-motion activity).

Each of the media-playback device 102 and the media-delivery system 104can include additional physical computer or hardware resources. In atleast some embodiments, the media-playback device 102 communicates withthe media-delivery system 104 via the network 106.

Although in FIG. 2 only a single media-playback device 102 andmedia-delivery system 104 are shown, in accordance with someembodiments, the media-delivery system 104 can support the simultaneoususe of multiple media-playback devices, and the media-playback devicecan simultaneously access media content from multiple media-deliverysystems. Additionally, although FIG. 2 illustrates a streaming mediabased system for cadence determination and media content selection,other embodiments are possible as well. For example, in someembodiments, the media-playback device 102 includes a media data store196 and the media-playback device 102 is configured to perform cadencedetermination and media content selection without accessing themedia-delivery system 104. Further in some embodiments, themedia-playback device 102 operates to store previously streamed mediacontent items in a local media data store.

In at least some embodiments, the media-delivery system 104 can be usedto stream, progressively download, or otherwise communicate music, otheraudio, video, or other forms of media content items to themedia-playback device 102 based on a cadence acquired by thecadence-acquiring device 160 of the media-playback device 102. Inaccordance with an embodiment, a user U can direct the input 172 to theuser interface 164 to issue requests, for example, to playback mediacontent corresponding to the cadence of a repetitive motion activity onthe media-playback device 102.

FIG. 3 is a schematic block diagram of the repetitive-motion activitycontent identification engine 238. In some embodiments, therepetitive-motion activity content identification engine 238 includes aplaylist analysis engine 240, a model building engine 242, a contentevaluation engine 244, a content selection engine 246, and a contentplayback analysis engine 248.

The playlist analysis engine 240 operates to analyze playlists toidentify media content items that users have identified as beingsuitable for playback during repetitive-motion activities (e.g., mediacontent items that are runnable). Example methods performed by someembodiments of the playlist analysis engine 240 are illustrated anddescribed with respect to at least FIG. 5.

The model building engine 242 operates to build one or more models thatcan be used to identify media content items for playback during one ormore types of repetitive-motion activities. In various embodiments, themodel building engine 242 uses one or more machine learning techniquesto build the models. Example methods performed by some embodiments ofthe model building engine 242 are illustrated and described with respectto at least FIG. 6.

The content evaluation engine 244 operates to evaluate media contentitems to determine whether the media content items may be suitable forplayback during one or more types of repetitive-motion activity. In someembodiments, a media content item is suitable for playback during arepetitive-motion activity if it is likely that the playback of themedia content item is likely to enhance a user's performance orenjoyment of the repetitive-motion activity. As another example, a mediacontent item that is conducive to the repetitive-motion activity issuitable for playback during the repetitive-motion activity. In someembodiments, the content evaluation engine 244 uses models generated bythe model building engine 242. Additionally, in some embodiments, thecontent evaluation engine 244 generates scores for media content itemsbased on the suitability of the media content items for playback duringone or more repetitive-motion activities. Example methods performed bysome embodiments of the content evaluation engine 244 are illustratedand described with respect to at least FIGS. 7-8.

The content selection engine 246 operates to select media content itemsfor playback during a repetitive-motion activity. In at least someembodiments, the content selection engine 246 filters media contentitems based on one or more characteristics including but not limited toa score generated by the content evaluation engine 244. Additionaldetails of some embodiments of the content selection engine 246 areillustrated and described with respect to at least FIGS. 10-17.

The content playback analysis engine 248 operates to analyze theplayback (or use) of media content items by users. In some embodiments,the content playback analysis engine 248 identifies media content itemsthat are frequently played back or skipped during repetitive-motionactivities. Additionally, in some embodiments, the content playbackanalysis engine 248 uses one or metrics related to the repetitive-motionactivity such as performance metrics, physiological metrics, andenhancement metrics. Examples of performance metrics include speed andcadence. Example physiological metrics include physiologicalmeasurements such as heart rate. Examples of enhancement metrics includecadence alignment to the media content. Other metrics that are analyzedby some embodiments include whether a user indicated liking a mediacontent item (e.g., by actuating a like control during playback or at alater time such as during a playlist review after completion of therepetitive-motion activity), whether the user added the media contentitem to a playlist, etc. Example methods performed by some embodimentsof the content selection engine 246 are illustrated and described withrespect to at least FIG. 9.

FIG. 4 illustrates an example method 270 of identifying media contentfor playback during a repetitive-motion activity performed by someembodiments of the repetitive-motion activity content identificationengine 238. Such a method can be used, for example, when the user isengaged in repetitive-motion activities, such as running, biking, orwalking. Media content, such as music, can impact the performance orenjoyment of such activities. For example, as noted above, music of afaster tempo can encourage the user U to run at a faster cadence.

At operation 272, a list of positive training examples of runnable mediacontent items is acquired. In some embodiments, the list is generated byanalyzing playlists of one or more users to identify media content itemsthat have been added to a running related playlist. In otherembodiments, a user may identify one or media content items he or sheenjoys running to.

At operation 274, a runnability model is built using the positivetraining examples. In various embodiments, the runnability model isbuilt using one or more machine learning techniques. Further, in someembodiments, the model is built based on audio analysis of the mediacontent items. Additionally, in some embodiments, the model is builtbased on metadata associated with the media content items. A runnabilitymodel is an example of a repetitive-motion model.

At operation 276, media content items are evaluated using therunnability model generated in operation 274. In some embodiments, someor all of the media content items stored in the media data store 196 areevaluated. Some embodiments evaluate a subset of the media content itemsbased on a characteristic such as a genre, era, popularity, tempo, etc.In some embodiments, a runnability score is generated for at least someof the evaluated media content items. In some embodiments, therunnability score is a value that corresponds to how similar a mediacontent item is to the positive training examples as calculated usingthe runnability model. In some embodiments, the runnability score is anumerical value in the range of 0-1 in which higher values indicate themedia content item is more similar to the positive training examplesthan a lower value. Some embodiments store the runnability scores in therepetitive-motion activity media metadata 232.

At operation 278, the evaluated media content items are filtered. Themedia content items may be filtered based on a variety ofcharacteristics, including a runnability score threshold, a genre, and atempo range. Additionally, some embodiments operate to filter mediacontent items based on analysis of audio signals associated with themedia content item. For example, media content items that include avariable tempo may be excluded. As another example, media content itemshaving quiet or low-energy portions with a duration greater than apredetermined threshold are excluded. However, in some embodiments ifthe quiet or low-energy portion is near the beginning or end of themedia content item, the media content item is not excluded. Instead, thequiet or low-energy portion may be excluded using mix-in or mix-outpoints. Examples of calculating and using mix-out and mix-in points areprovided in U.S. Patent Application Ser. No. 62/163,865, filed on May19, 2015, and U.S. patent application Ser. No. 14/944,972, filed on Nov.18, 2015, both of which are titled SYSTEM FOR MANAGING TRANSITIONSBETWEEN MEDIA CONTENT ITEMS, the entireties of which are herebyincorporated by reference.

In some embodiments, the media content items that pass all of thefilters are identified as runnable and a runnable flag (e.g., a Booleanvalue field) in the repetitive-motion activity metadata associated withthe media content item. Alternatively, the runnability score of mediacontent items that do not pass the filters may be adjusted (e.g.,lowered or set to zero).

Although the method 270 has been described sequentially, in someembodiments the operations of method 270 are performed in differentorders or include different operations. Additionally, in someembodiments, the operations of method 270 may be performed at differenttimes or repeated independent of each other. For example, in someembodiments, operations 272 and 274 are repeated on a regular schedule(e.g., weekly, monthly, annually, etc.) to generate or update a list ofrunnable songs and the runnability model built from that list. Whileoperations 276 and 278, on the other hand, are performed once initiallyon all media content items in the media data store 196 and is thenrepeated on new media content items as those new media content items areadded to the media data store 196. Additionally, some embodiments do notperform operation 278.

FIG. 5 illustrates an example method 310 of acquiring a list of positiveexamples of runnable media content items that is performed by someembodiments of the playlist analysis engine 240. Such a method can beused, for example, to identify media content items as runnable based onthe playlists users have created.

At operation 312, playlists that appear related to running areidentified as source playlists. The playlists may be identified byanalyzing the playlists 216. In some embodiments, source playlists areidentified based on the inclusion of certain words or phrases in a titleor description associated with the playlist. For example, words that arerelated to running (e.g., run, running, jog, marathon, 5 k, etc.) may beused to identify source playlists. Additionally, in some embodiments,words that relate to fitness (work out, health club, training, etc.) arealso used to identify source playlists. Furthermore, in someembodiments, words that relate to other types of repetitive-motionactivities are also used to identify source playlists.

At operation 314, a list of potential example media content items isgenerated based on the source playlists. In some embodiments, all mediacontent items appearing in at least a predetermined number of playlistsare included in the list. In embodiments, the predetermined number is 1,2, 5, 10, 50, 100, 500, or another number.

Further, some embodiments analyze the source playlists to furtherdetermine the relevance of the playlist to running. The analysis may bebased on many factors including the words that were used to identify thesource playlist, whether the words appeared in a title or a description,the curator of the playlist, the number of users that follow theplaylist, the number of times the playlist has been played, etc. In someembodiments, a weighting scheme is used to calculate a weight value forthe source playlists. Example weighting schemes used in some embodimentinclude: weighting a playlist that includes words associated withrunning higher than a playlist that includes words associated withfitness or another repetitive-motion activity; weighting a playlist thatincludes a relevant word in a title higher than a playlist that includesa relevant word in a description; weighting a playlist curated by astreaming service (or professional curator) higher than a playlistcurated by a user (or vice versa); weighting a playlist with morefollowers higher than a playlist with fewer followers; weighting aplaylist that has been played more times higher than a playlist that hasbeen played fewer times. In some embodiments, the weighted values of thesource playlists that include a particular potential example mediacontent item are summed (or otherwise combined) and the resulting value(referred to as a positive playlist inclusion score herein) is comparedto a predetermined threshold. The potential example media content itemswith a positive playlist inclusion score that exceeds the threshold maybe analyzed further as described below.

At operation 316, it is determined whether the potential example mediacontent items are included in playlists that appear inappropriate forrunning. In some embodiments, playlists are identified as inappropriatefor running based on the inclusion of words or phrases in a title thatare related to relaxing (e.g., calming, chill, relax, wind down, sleep,calm, etc.). In some embodiments, a negative playlist inclusion score iscalculated for the potential example media content items based on beingincluded in playlists that are identified as not being appropriate forrunning. The negative playlist inclusion score for a potential examplemedia content item is calculated in a similar manner and according tosimilar factors as the positive playlist inclusion score.

At operation 318, a combined playlist inclusion score is calculated forthe potential example media content items included in the list based onthe playlists in which the potential example media content items areincluded. In some embodiments, the combined playlist inclusion score iscalculated as a ratio of the positive playlist inclusion score to thenegative playlist inclusion score. In other embodiments, the combinedplaylist inclusion score is calculated otherwise, such as by calculatinga difference between the positive playlist inclusion score and thenegative playlist inclusion score. Further, in some embodiments, thecombined playlist inclusion score is calculated as a difference betweenor ratio of the number of playlists that appear related to running andthe number of playlists that appear inappropriate for running in whichthe media content item is included.

At operation 320, potential example media content items are selected aspositive example media content items based upon the combined playlistinclusion score In some embodiments, potential example media contentitems that have a combined playlist inclusion score exceeding apredetermined threshold are selected as positive examples of runnablemedia content items. As an example, when the combined playlist inclusionscore is calculated as a ratio, the predetermined threshold is two,three, four, five, or ten in some embodiments. Other embodiments use apredetermined threshold in a range of one to twenty-five. Additionally,in some embodiments, a predetermined number of media content itemshaving the highest combined playlist inclusion scores are selected aspositive examples.

FIG. 6 illustrates an example method 350 of building a runnability modelbased on positive examples of runnable media content items that isperformed by some embodiments of the model building engine 242. Such amethod can be used, for example, to build a model for classifying oridentifying additional media content items as runnable.

At operation 352, characteristics of the audio signals of the positiveexamples of runnable media content items are determined. In someembodiments, the audio signals of the positive examples are analyzed todetermine the characteristics. Additionally, in some embodiments, someor all of the characteristics of the audio signals are retrieved fromthe media content metadata 214 or elsewhere.

Example characteristics determined by some embodiments include anaverage duration of a musical event such as a single note or othermusical event, a tempo regularity, a percussivity, and a beat strength.In some embodiments, the average duration of a musical event iscalculated in various ways, including by dividing a total number ofmusical events in a media content item by a duration of the mediacontent item. The tempo regularity corresponds to the consistency of thebeat in a media content item. In some embodiments, the tempo regularityis based on calculating a standard deviation or variance value formeasurements of the tempo over multiple intervals of a media contentitem. The percussivity corresponds to the strength or contribution ofpercussive instruments (or synthesized equivalents) to the media contentitem. The beat strength is proportional to the loudness of musicalevents that happen in correspondence to a beat. Some embodiments alsoinclude characteristics that are determined by other machine learningmodels. For example, some embodiments, include an energy characteristicthat is calculated by a machine learning model trained to rate therelative energy levels of various media content items similarly to auser's rating of the energy level. Other embodiments determineadditional, different, or fewer characteristics.

At operation 354, the determined characteristics are used to build astatistical model that relates the determined characteristics to asimilarity value to the positive examples. In some embodiments, themodel is a function or equation that operates on the values of thevarious characteristics to calculate a value corresponding to thesimilarity to the positive examples. In some embodiments, the modelrepresents each characteristic as a dimension in a multi-dimensionalspace. Further, in some embodiments, the model defines an equation tocompute the likelihood of a media content item being similar to thepositive examples as far as the modeled characteristics are concerned.

In some embodiments, various machine learning techniques are used togenerate the model. For example, in some embodiments, the model isgenerated using a variational Bayes Gaussian mixture model. In otherembodiments, other machine learning techniques are used as well.

FIG. 7 illustrates an example method 380 of evaluating media contentitems using a runnability model that is performed by some embodiments ofthe content evaluation engine 244. Such a method can be used, forexample, to calculate a runnability score for media content items.

At operation 382, characteristics of the audio signal of a media contentitem that is being evaluated are determined. The operation 382 issimilar to the operation 352 except that the characteristics aredetermined for the media content item that is being evaluated ratherthan the positive examples.

At operation 384, a runnability score is calculated using therunnability model and the determined characteristics. As noted above,the runnability model operates to calculate a value that corresponds tothe similarity between the characteristics of the media content itembeing classified and the characteristics of the positive examples usedto generate the model. In some embodiments, the value calculated usingthe runnability model is scaled to a numeric value between 0-1.Alternatively, the runnability score may be a Boolean value representingwhether the value calculated by the runnability model satisfies apredetermined threshold for identifying a media content item as beingrunnable.

At operation 386, the runnability score is stored. In some embodiments,the runnability score is stored in the repetitive-motion activity mediametadata 232 or the media content metadata 214.

FIG. 8 illustrates an example method 410 of filtering a media contentitem for runnability that is performed by some embodiments of thecontent evaluation engine 244. Such a method can be used, for example,to exclude certain media content items that are unlikely to be suitablefor playback during running.

At operation 412, characteristics of the audio signal of a media contentitem that is being evaluated are determined. The operation 412 issimilar to the operations 352 and 382, however in some embodimentsdifferent or additional characteristics are determined. In someexamples, a tempo regularity value is calculated as described above withrespect to at least the operation 352 of FIG. 6. In addition, in someembodiments, a maximum duration of quietness and a maximum duration oflower energy are determined as well. In some embodiments, the maximumduration of quietness is based on a threshold volume level and operatesto identify a maximum consecutive duration of the media content itemthat is below the threshold volume level. Similarly, the maximumduration of lower energy is based on a threshold volume level andoperates to identify a maximum consecutive duration of the media contentitem that is below the threshold energy level. In some embodiments, themaximum duration of quietness and maximum duration of lower energy arecalculated for a portion of the media content item identified by amix-in point and mix-out point. Examples of calculating and using anenergy level of a portion of a media content item are also provided inU.S. Patent Application Ser. No. 62/163,865 and Ser. No. 14/944,972,discussed above.

At operation 414, the determined audio characteristics are analyzed todetermine if one or more audio signal criteria are met. The audio signalfilters may operate to exclude media content items having audio signalcharacteristics that do not meet certain predetermined threshold values.Embodiments include one or more of the following example audio signalfilters: a tempo regularity filter that operates to exclude mediacontent items that do not meet a predetermined threshold for temporegularity; a quiet gap filter that operates to exclude media contentitems that have a maximum duration of quietness that exceeds apredetermined threshold value; and a low-energy gap filter that operatesto exclude media content items that have a maximum duration of lowenergy that exceeds a predetermined threshold value. Other embodimentsinclude additional, different, or fewer audio signal filters.

At operation 416, the metadata for the media content item beingevaluated is analyzed to determine if metadata filter criteria are met.In some embodiments, the metadata for the media content item beingevaluated is retrieved from the media content metadata 214, therepetitive-motion activity media metadata 232, or elsewhere.

The metadata filters may operate to exclude media content items havingmetadata characteristics. Some embodiments include a genre filter thatoperates to exclude media content items of a particular genre (e.g.,children's music or holiday music). Other embodiments includeadditional, different, or fewer metadata filters.

At operation 418, a stored runnability score associated with the mediacontent item being evaluated is updated. For example, in someembodiments, if the media content item failed either the audio signalfilters (operation 414) or the metadata filters (operation 416) then therunnability score is reduced or set to zero. Additionally, in someembodiments, a field is stored separately from the runnability score tocategorically block (e.g., blacklist) media content items that fail topass at least some of the filters discussed herein.

FIG. 9 illustrates an example method 450 of analyzing the playback ofmedia content items during running that is performed by some embodimentsof the content playback analysis engine 248. Such a method can be used,for example, to identify media content items as runnable based onanalyzing the playback of the media content items during running (oradditionally or alternatively, in some embodiments, otherrepetitive-motion activities). The method 450 can be used to identifymedia content items that have positive effects on running. The method450 can also be used to identify media content items that have negativeeffects on running. In some embodiments, the media content items havinga positive effect are identified as positive examples for use inbuilding or updating a runnability model as illustrated and describedwith respect to at least FIG. 6. Additionally, in some embodiments, therunnability scores of media content items that are identified as havinga strong positive or negative effect are updated by the method 450.

At operation 452, measurements related to running while a particularmedia content item is being played back are received. In variousembodiments, various measurements are received. In some embodiments,some or all of the measurements are captured by the media-playbackdevice and transmitted to the media-delivery system 104. Examplemeasurements include cadence, pace, cadence phase alignment to the mediacontent item, and various physiological measurements. Examples ofcalculating cadence phase alignment to the media content item areprovided in U.S. Patent Application Ser. No. 62/163,856, filed on May19, 2015, and U.S. patent application Ser. No. 14/883,318, filed on Oct.14, 2014, both of which are titled CADENCE AND MEDIA CONTENT PHASEALIGNMENT, the entireties of which are hereby incorporated by reference.Examples of capturing and using physiological measurements are providedin U.S. Patent Application Ser. No. 62/163,915, filed on May 19, 2015,and U.S. patent application Ser. No. 14/883,245, filed on Oct. 14, 2015,both of which are titled HEART RATE CONTROL BASED UPON MEDIA CONTENT

SELECTION, the entireties of which are hereby incorporated by reference.In some embodiments, pace is calculated from cadence with an estimatedor calibrated stride length. Additionally, pace can be calculated usingthe location-determining device 150.

Furthermore, in some embodiments the received measurements relate to asingle user. Additionally, in some embodiments, the receivedmeasurements relate to multiple users and are received from multiplemedia-playback devices. In some embodiment, the measurements arereceived and captured for a time period (e.g., a week, a month, twomonths, three months, six months, etc.).

At operation 454, the suitability of the media content items forplayback during running is evaluated based on the received measurements.In some embodiments, a score is generated that corresponds to thesuitability of a particular media content item for playback duringrunning. In some embodiments, the suitability of a media content item iscalculated based on comparing the captured measurements to a targetvalue for the parameter being measured. For example, if the user hasindicated a desire to run with a cadence of 180 steps per minute, mediacontent items that were played back while measurements of cadence thatare close to 180 steps per minute were captured may be considered topositively affect the repetitive-motion activity. In some embodiments,the media content items are compared to one another to determine whichmedia content items have a greater positive effect on therepetitive-motion activity. Beneficially, this comparative evaluationcan be helpful to differentiate the effect of the media content itemfrom the user's underlying performance or ability. Additionally, in someembodiments, media content items are evaluated based in part oncalculating a metric related to how much the measurements change duringplayback of the media content item (e.g., standard deviation orvariance). Further, in some embodiments, the media content items areevaluated based on whether users indicate liking a media content item(e.g., by actuating a like or favorite control) or disliking the mediacontent item (e.g., by actuating a dislike control or skipping the mediacontent item) when it is played during running.

At operation 456, at least some of the media content items for whichmeasurements were received are identified as positive examples ofrunnable media content items. In some embodiments, the media contentitems are compared to a predetermined threshold for a suitability score.Additionally, in some embodiments, a predetermined number of the highestscoring media content items are selected as positive examples. The newlyselected positive examples may be included with other previouslyselected positive examples or may be used to replace the previouslyselected positive examples.

At operation 458, a runnability score for the media content items forwhich measurements were received is updated based on whether it wasdetermined that the media content item has a positive or negative effecton running. For example, the runnability score for a particular mediacontent item is increased if it is determined that the media contentitem has a positive effect on running. Conversely, the runnability scorefor a particular media content item is decreased if it is determinedthat the media content item has a negative effect on running.

FIGS. 10-17, which are discussed below, illustrate examples relating toselecting media content for playback. In some embodiments, some or allof the methods disclosed in FIGS. 10-17 are performed by the contentselection engine 246. Alternatively or additionally, in someembodiments, some or all of the methods disclosed in FIGS. 10-17 areperformed by the tempo search control engine 168 on the media-playbackdevice 102. Further, in some embodiments, the content selection engine246 and the tempo search control engine 168 together perform some or allof the methods disclosed with respect to FIGS. 10-17.

FIG. 10 illustrates an example method 550 of selecting media contentplayed by the media-playback device 102 based upon a tempo of the mediacontent. Such a method can be used, for example, when the user isengaged in repetitive motion activities, such as running or walking.Media content, such as music, can impact the performance of suchactivities. For example, as noted above, music of a faster tempo canencourage the user U to run at a faster cadence and vice versa.

At the step 552 of the method 550, the tempo search control engine 168of the media-playback device 102 receives a selection of media content.This selection can take a variety of forms.

For example, the user U can simply identify certain media content of adesired tempo. In another example, the user can identify a desired tempoitself (e.g., 90 beats per minute), as shown in FIG. 14 and describedfurther below.

In yet other examples, one or more automated processes can be used toselect certain media content and/or a tempo. For example, variousattributes of the user, such as the user's physiological state, can beused as an automated process for selecting a desired tempo. An exampleof such processes are described in U.S. Patent Application Ser. No.62/163,915, filed on May 19, 2015, and U.S. patent application Ser. No.14/883,245, filed on Oct. 14, 2015, both of which are titled HEART RATECONTROL BASED UPON MEDIA CONTENT SELECTION, the entireties of which arehereby incorporated by reference. This patent application describes howphysiological measurements, such as heart rate, and non-physiologicalmeasurements, such as location, can be used to automatically select adesired tempo for the user U.

Next, at step 554, the selected media content is analyzed to identify arelevant tempo. Various processes can be used to analyze media content,such as music, to determine a tempo. See FIG. 11 for more details.

Finally, at step 556, additional media content meeting the desired temporequirements are provided to the user, such as in a graphical userinterface. One example of such a user interface is described withreference to FIG. 14 below. Other examples are provided in U.S. PatentApplication Ser. No. 62/163,887, filed on May 19, 2015, and U.S. patentapplication Ser. No. 14/883,273, filed on Oct. 14, 2015, both of whichare titled MULTI-TRACK PLAYBACK OF MEDIA CONTENT DURING REPETITIVEMOTION ACTIVITIES, the entireties of which are hereby incorporated byreference. In that patent application, a grid interface for displayingmedia content options based on tempo is disclosed.

In one context, the steps 554, 556 are performed by the media-deliverysystem 104 in response to a request from the tempo search control engine168. For example, the tempo search control engine 168 can receive anindication of a desired tempo (either through selected media content ora specified tempo), and the media-delivery system 104 is configured toperform the steps 554, 556 and deliver media content of the desiredtempo back to the media-playback device 102 for consumption by the userU.

FIG. 11 illustrates a more detailed example of the step 554 of FIG. 10,in which media content is analyzed to identify tempo. As noted, themedia-delivery system 104 can perform the step 554 based upon a requestfrom the media-playback device 102.

At step 562, the relevant tempo is identified. In this context, themedia content provided by the user U is analyzed to determine the tempoof the media content. This process is described below.

As noted, in other examples, the user can simply select a desired tempoinstead of providing media content, or a tempo can automatically beprovided based upon analysis of such criteria as physiological aspectsof the user U, like heart rate.

Next, at step 564, the media content in the media server application 184is filtered based upon certain criteria. In one example, each mediacontent item stored by the media server application 184 is analyzed andparticular additional metadata for each item is stored by the mediacontent metadata 214.

This metadata may include such information as a tempo for the mediacontent item. In one example, each media content item is analyzed, and atempo is associated with the metadata for the item.

A tempo of a media content item can be determined in various knownmanners. In the example of songs, a tempo of a song can be relativelyeasily identified because songs typically have a steady tempo throughouttheir entire playing time. Where a tempo changes significantlythroughout a song, in some embodiments, such variations in tempo can beaveraged to represent a single tempo of the song. In other examples, aportion of the song having an approximately constant tempo can beidentified, and such a constant tempo can be used as a tempo for theentire song. In yet other examples, the portion of the song having anapproximately constant tempo is taken and used to replace the entiresong while the other portion of the song, which has variable tempo, areexcluded from playback. Other methods of obtaining a tempo of a song arealso possible.

Additional details on determining a tempo for media content is providedin U.S. Patent Application Ser. No. 62/163,845, filed on May 19, 2015,and U.S. patent application Ser. No. 14/883,298, filed on Oct. 14, 2015,both of which are titled CADENCE-BASED PLAYLISTS MANAGEMENT SYSTEM, theentireties of which are hereby incorporated by reference.

The metadata may also include other criteria, such as a runnabilityscore for each media content item. A runnability score can be calculatedbased upon a variety of methods and factors, such as comparison to othercontent known to have desired characteristics or properties that aresuitable for running. Examples relating to calculating runnabilityscores are illustrated and described with respect to at least FIGS. 3-9.Additional details regarding how a runnability score can be calculatedfor a media content item are provided in U.S. Patent Application Ser.No. 62/163,921, filed on May 19, 2015, and U.S. patent application Ser.No. 14/934,008 filed on Nov. 18, 2015, both of which are titledIDENTIFYING MEDIA CONTENT, the entireties of which are herebyincorporated by reference.

Additional details on the filtering of the step 564 are provided in theexample illustrated and described with reference to FIG. 12.

Finally, at step 566, filtered media content items having the desiredtempo are selected. Additional details on this process are provided withreference to FIG. 13.

FIG. 12 illustrates a more detailed example of the step 564, shown inFIG. 11, involving the filtering of the media content.

At step 572, the runnability score for a particular media content itemis determined. This can be accomplished using the processes describedpreviously herein. Alternatively, the runnability score may be stored aspart of the metadata associated with the media content item.

Next, at step 574, a determination is made regarding whether or not theuser previously rated the particular media content item. For example, asdescribed further below, the user U can rate a particular media contentitem as one the user likes or dislikes. If the user has previously likedthe particular media content item, control is passed to step 576, and adecreased threshold filter is used. Otherwise, control is passed to step578, and a standard filter threshold is used.

For example, if the user has previously “liked” the particular mediacontent item, the decreased threshold filter may require that therunnability score for the media content item be at least 0.4.Alternatively, if the media content item has not been previously rated,the standard threshold filter may require that the runnability score forthe media content item be at least 0.6. In this manner, media contentitems that were previously marked as “liked” are favored. Otherthreshold values can be used in other embodiments. A decreased thresholdfilter has a threshold value that is less than the threshold value ofthe standard filter threshold.

Next, at step 580, the relevant filter (i.e., decreased or standardthreshold) is applied. If the media content item exceeds the thresholdof the applied filter, control is passed to step 582, and the mediacontent item is recommended to the user. Otherwise, control is passed tostep 584, and the media content item is not recommended.

Referring now to FIG. 13, additional details are provided about the step566 of filtering the media content based upon tempo.

At step 590, a desired tempo range is determined. This range is basedupon the desired tempo that is either calculated from the media contentselected by the user, the tempo that is specifically selected by theuser, and/or by one or more automated processes of determining a desiredtempo, such as by an analysis of heart rate, as described above.

The desired tempo is converted to a range of tempos, as is noted above.In some examples, this range can have various scales, such as 2 beats, 5beats, 10 beats, etc. For example, if the desired tempo is 178, thattempo is converted to a range of 175-180.

In another example, the system is programmed to define a target numberof songs to return, such as 5, 10, 15, 20, 50 100. For instance, if thetarget is to return 10 songs around 178 BPM, and given the number ofsongs available around that tempo, the resolution or range for the tempocan be modified, such as low as +/−0.5 BPM or a large as +/−7 BPM. Thedynamic range is dependent on the size of the portfolio of mediacontent, which can be driven by such factors as how the runnabilityscores are calculated. In other examples, the songs are simply ranksbased upon how far each song is from the desired tempo, and the desirednumber of songs is selected based upon that ranking. In other instances,the range to find desired content is dynamically calculated based uponmultiple factors, such as size of portfolio and runnability.

This range is then used at step 592 to filter the media content.Specifically, the tempo of each media content item is analyzed todetermine if the tempo fails within the range. As noted above, eachmedia content item may include metadata defining the tempo for thatmedia content item. The tempo metadata can be used to determine if themedia content item falls within the desired tempo range.

The media content falling within the desired range is then analyzed atstep 594 to determine if sufficient media content has been identified.For example, if the range is a tempo of 175-180 beats per minute,perhaps 1000 songs fall within that range. In such an instance, asufficient amount of media content has been identified, and control isprovided to step 596, where the process continues.

However, if the number of media content items with the desired tempo issmall, such as less than 100, less than 50, less than 20, less than 10,and/or less than 5, control can instead be passed to step 598, and thefiltering of the content media can be modified.

One possible modification is to expand the range of tempos. In theexample, the range could be expanded to be 170-185 in order to attemptto capture more media content items. In another example, otherthresholds, such as the thresholds associated with the runnabilityscores used in the step 564 can be loosened. In yet another example, thetempo ranges can be halved or doubled (as noted above), which can resultin tempos that provide similar guidance when running. Otherconfigurations are possible.

Once the filters have been modified, control is passed back to the step590 and the step 566 of filtering the media content based upon tempo isperformed again with the modified filters.

Although filtering based upon criteria such as runnability and tempo areprovided in the examples, other criteria can also be used to filter themedia content. Examples of other criteria include the strength of thebeat, the general energy of the content, the similarity of the contentto other items. Other filtering based upon such criteria as desiredgenre, mood, or age (i.e., era) can also be used.

Referring now to FIG. 14, an example tempo-based content playback screen640 displayed by some embodiments of the user interface 164 of themedia-playback device 102 is shown. In some embodiments, the tempo-basedcontent playback screen 640 is displayed in response to receiving arequest by the user to select media content of a desired tempo.

In some embodiments, the tempo-based content playback screen 640includes a playlist information message 642, a previous media contentitem display panel 644, a current media content item display panel 646,a next media content item display panel 648, a current media contentitem information message 650, a dislike control 652, a like control 654,a tempo information message 656, and a pause control 658.

The playlist information message 642 operates to display informationabout the currently playing playlist of media content items. Theplaylist may be a pre-defined playlist of media content items thatcorrespond to an acquired cadence or an ad-hoc playlist generated by themedia-playback device 102 or the media-delivery system 104 based on theacquired cadence. In some embodiments, the playlist information message642 displays a title provided with a pre-defined playlist (e.g. “OutdoorRunning,” “Spin Class,” “Peak Performance,” etc.). In some embodiments,the playlist information message 642 displays information that relatesto the media content items included in an ad-hoc playlist such as aregion (e.g., Sweden, Europe, U.S., etc.), a genre (e.g., Country, Rock,Rap, etc.), a mood (e.g., calm, happy, etc.), an era (e.g., 70's, 80's,90's, 00's, etc.), or popularity (e.g., Top 50, etc.).

The previous media content item display panel 644 operates to displayinformation about the previously played media content item such as animage associated with the previously played media content item (e.g., analbum cover, an artist photograph, etc.). The current media content itemdisplay panel 646 and the next media content item display panel 648operate similarly with respect to the currently playing media contentitem and the media content item that is schedule to play next. In someembodiments, the user interface 164 operates to cause the next orprevious media content item to begin playback in response to detecting aswipe input to the left or right over the current media content itemdisplay panel 646. Additionally, in some embodiments, the user interface164 operates to cause the previously played media content item to beginplayback in response to detecting a touch input on the previous mediacontent item display panel 644. Similarly, in some embodiments, the userinterface 164 operates to cause the next media content item to beginplayback in response to detecting a touch input on the next mediacontent item display panel 648.

The current media content item information message 650 operates todisplay information about the currently playing media content item. Someembodiments display one or more of the following: a title, an artistname, an album name, a current position, a total length, and a tempo.

The dislike control 652 and the like control 654 operate to receiveinputs indicating that a user dislikes or likes the currently playingmedia content item. In some embodiments, the media-playback devicestores a like/dislike value associated with the currently playing mediacontent item upon actuation of either the dislike control 652 or thelike control 654. The value may be stored locally on the media-playbackdevice 102 or remotely on the media-delivery system 104 or elsewhere. Insome embodiments, one or both of the media-playback device 102 and themedia-delivery system 104 use the like/dislike values that have beenpreviously stored in selecting media content items for future playback.Additionally, in at least some embodiments, upon actuation of thedislike control 652, the currently playing media content item stopsplaying immediately or after a period of time and a new media contentitem begins to play.

The tempo information message 656 operates to present information to theuser about the tempo of the current media content item. In someembodiments, the tempo information message 656 displays a numeric valuerepresenting the user U's current tempo selection.

As noted, in some embodiments, the user can select a particular desiredtempo manually. In such an example, the user can select the tempoinformation message 656 to bring up increase/decrease tempo arrowsand/or a keypad that allows the user to directly input a desired tempo.Once the desired tempo is inputted by the user, the system can providemedia content and/or samples at the desired tempo for the user.

Additionally, in some embodiments, the tempo information message 656also presents information related to whether the tempo has recentlychanged. For example, the tempo information message 656 may include anarrow pointing up if the tempo has recently increased and an arrowpointing down if the tempo has recently decreased.

Alternatively, the tempo may be displayed in a first color to indicate arecent increase, a second color to indicate a recent decrease, and athird color to indicate a stable tempo. As yet another alternative, thetempo information message 656 may blink or otherwise indicate theoccurrence of a recent change in tempo. In yet another embodiment, thetempo information message 656 may operate to indicate how a currenttempo compares to a goal or target cadence using any of the previouslymentioned visual indicators.

In yet other examples, information in addition to or other than tempocan be displayed. For example, the information message 656 can beconfigured to display the user U's heart rate. In yet another example,the information message 656 can be configured to display an estimatedcadence, as described in U.S. Patent Application Ser. No. 62/163,840,filed on May 19, 2015, and U.S. patent application Ser. No. 14/883,232,both of which are titled CADENCE DETERMINATION AND MEDIA CONTENTSELECTION, the entireties of which are hereby incorporated by reference.

The pause control 658 operates to receive a pause input from the user.In some embodiments, the pause input triggers the media-playback device102 to pause cadence acquisition. Beneficially, by pausing cadenceacquisition, the user can take a break or otherwise change cadencewithout causing the playback of media content items to change. Users maytake break for many reasons, such as to rest/recover, to wait to safelycross an intersection, or to wait for a running partner. Users maytemporarily change cadence for many reasons as well such as to climb astair case or a big hill, to cross rugged terrain, to weave throughheavy traffic, etc. Additionally, in some embodiments, the pause inputalso triggers the media-playback device 102 to pause playback of themedia content items.

Referring now to FIG. 15, the media content for a user can be selectedin various ways. As illustrated, a corpus or universe 700 of mediacontent can be sorted into pools that are thereupon used to fill bucketsof particular tempos.

The universe 700 can be formed in various ways. In some examples, mediacontent of various genres are eliminated. For example, tracks from somegenres, such as Christmas music, children's music, etc., are removedfrom the universe 700. In another example, some genre-specificattributes can be used. For example, for tracks from a genre like jazzmusic, the suitability of a particular song (e.g., its runnabilityscore) may be set higher than the suitability of a song from anothergenre, such as heavy metal music. In yet another example, media contenthaving a runnability score less than a threshold is excluded. Otherexamples exist.

In the example of FIG. 15, a first pool 702 includes media content of afirst variety. This media content can be selected based upon variousattributes, such as whether or not the user has previouslyviewed/listened to the media content, liked the media content, etc.Media content can also be sorted into second and third pools 704, 706that have different attributes, such as media content that is related tomedia content that the user has previously viewed/listened to, and mediacontent that has no known relationship with media content the user haspreviously viewed/listened to. In such an example, the pool 702 wouldhave media content that is likely to be most relevant to the user. Next,the pool 704 has media content that may be relevant based upon itsrelationship to the media content in the pool 702. And, the pool 706 hasno specified relationship to the media content in the pool 702 andtherefore may have the least likelihood to be relevant to the user.

A more specific example is given in FIG. 16. In this example, a universe710 of media content is divided into pools 712, 714, 716, 718. Each ofthese pools has media content of specific attributes.

For example, the pool 712 includes media content that the user haspreviously viewed/listened to or otherwise indicated as being desirable.For example, the pool 712 can include tracks for songs from the user'spersonal media list.

The pool 714 includes media content from the same artists as the mediacontent that is in pool 712. For example, assume that the user hasviewed/listened to media content from artist A. The pool 714 can includeother media content that the user has not yet viewed/listened to but isfrom the same artist A.

The pool 716 includes media content that is similar to the media contentthat the user has already viewed/listened to. For example, variousartists can be clustered into different groups based upon similarity.The pool 716 can be populated with media content from artists that aresimilar to the artists that the user has already viewed/listened tomedia content from.

Finally, the pool 718 includes new media content. This new media contentdoes not necessarily have a specified relationship with media contentthat the user has already viewed/listened to. Instead, the new mediacontent is media content that is selected based upon other criteria,such as a relationship to selected media content attributed usingartificial learning algorithms or other machine learning techniques.

For example, machine learning techniques can be used to select the mediacontent in the pool 716. One such example technique is the word2vectortool that can be used to select media content based upon the user'splaylists or other lists of media content. In this example, theword2vector tool is used to analyze the corpus of the user's existingmedia content in order to select new media content this is related.Specifically, the word2vector tool can be used to generate a resultingword vector file that is used by machine learning applications to selectother media content that is similar to the media content that hasalready been viewed/listened to by the user. Other similar processes canalso be used.

Referring now to FIG. 17, once the pools of media content are formed,the pools are used to populate playlists for the user based upon thetempo of each media content item. In this example, tempo search controlengine 168 (and/or system 104) is programmed to place the media contentinto buckets representing various tempos, such as 140 beats per minutethrough 190 beats per minute. The buckets can be formed for each 5beats, so that there are buckets for 140, 145, 150, 155 . . . 190 beatsper minute. Other examples can be provided, such as various buckets forgreater tempos (e.g., 195, 200, 205 . . . n beats per minute) or variousdifferent gradations (e.g., buckets for every beat, such as 140, 141,142 . . . 190, or every ten beats, such as 140, 150, 160 . . . 190).

As shown in FIG. 17, one example process 750 for filling the bucketsincludes selecting a specific number of media content items from eachpool and populating those items in each bucket. At operation 752, thebuckets are filled with media content from a particular pool. Next, atoperation 754, a determination is made regarding whether or not thebuckets have reached a threshold amount. For example, a threshold can beset for each bucket for each type of media content from a particularpool. Once this threshold is reached for each bucket, control is passedto operation 756, and media content from the next pool (“n+1”) ispulled. This process continues until the buckets are full and/or themedia content from all of the pools is exhausted. If a particular bucketlacks a certain number of media content items when the process iscompleted, the remainder and/or the entire bucket can be populated withgeneral purpose media content that has not been selected based upon theuser's preferences. This assures that each bucket has at least aspecified number of media content items to accommodate all of thedesired tempos.

In the given example, the process 750 can be done as follows. First, themedia content from the pool 712 (actively listened media content) is putinto buckets by determining the tempo for each media content item (perthe process described above) and assigning each media content item tothe respective bucket. Next, the pool 714 (new media content from sameartists) is distributed in the same manner. The process continues untileach bucket reaches a threshold amount, such as 20 or 25 media contentitems for each bucket. In one example, the outer buckets 140, 190 have athreshold of 20 media content items, and the inner buckets 145-185 havea threshold of 25 media content items, so that more media content itemsare included at the inner buckets where it is more likely that temposwill be used more often (than at the extremes). Different thresholdvalues can be used, such as different values for different bucketsdepending on the popularity of certain tempos (i.e., buckets having morepopular tempos will have an increased number of items).

Once the media content is placed into the buckets, the ordering of themedia content within each bucket can be defined using various methods.In one example, the media content from pool 712 is placed first, thenmedia content from pool 714, etc. In this manner, the user gets mediacontent at each tempo that means the most to the user. The user'splaylist loops through the media content according to that order untilexhausted. At that time, the loop can start over with the media contentform the pool 712.

In another example, the media content is ordered in a more randommanner. In one example, the order can be completely random withoutregard for which pool the media content originates. In another example,each media content item can be assigned a value, with the value beinghigher depending on the originating pool. This makes it more likely thata media content item from the pool 712 will be selected, but mediacontent from the pool 718 can be selected too depending on the specificalgorithm used. In other examples, other attributes can be used toselect media content within a bucket, such as selecting media content tovary genres or highlight a certain genre.

In other examples, other filtering can be used. For example, limits onthe number of media content items in each bucket can be imposed basedupon criteria such as (in the instance of music), songwriter, singer,genre-type, etc. Such limits would assure that one or more buckets arenot dominated by a single singer, thereby increasing variety for theuser. Other filtering can be used.

The media content can be delivered to the user in various ways. In oneexample, the media content is delivered in chunks of five media contentitems. Other mechanisms can be used. Further, the pools can be updatedat various stages, such as daily, weekly, etc. and the bucketsrepopulated accordingly. In this manner, the media content items do notbecome stale, and the user's changes in tastes can be accommodated.

As noted previously, although many of the examples provided above aredescribed with respect to running, other embodiments relate to otherrepetitive motion activities as well such as cycling, swimming, androwing.

This application is related to U.S. Ser. No. 62/163,927, filed on May19, 2015, and to U.S. Ser. No. 14/883,295, filed on Oct. 14, 2015, bothof which are entitled SEARCH MEDIA CONTENT BASED UPON TEMPO, and whichapplications are hereby incorporated by reference in their entireties.

The examples provided herein result in a more efficient system formanaging and delivering media content items. For example, by placing theitems into buckets, the items can be delivered more efficiently andon-demand by the system. Further, the methods and interfaces used toaccess the items can be optimized to allow the systems to function innear real-time to deliver the media content items for consumption.

The various embodiments described above are provided by way ofillustration only and should not be construed to limit the claimsattached hereto. Those skilled in the art will readily recognize variousmodifications and changes that may be made without following the exampleembodiments and applications illustrated and described herein, andwithout departing from the true spirit and scope of the followingdisclosure.

What is claimed is:
 1. A media system, comprising: a media-playbackdevice including: a media-output device that plays media content items;wherein the media-playback device is configured to: select media contentitems for playback during a repetitive-motion activity by sorting auniverse of media content into buckets of tempos, and subsequentlyselecting the media content items from a bucket associated with acadence of the repetitive-motion activity, the bucket generated by:filtering media content according to the repetitive-motion activity;placing each media content item from the filtered media content into twoor more pools of media content, the two or more pools comprising: afirst pool including media content that is from the filtered mediacontent for the repetitive-motion activity and that a user haspreviously listened to using the media system; and a second poolincluding media content that is from the filtered media content for therepetitive-motion activity, and that the user has not previouslylistened to using the media system and is related to the media contentin the first pool; and filling the bucket with media content selectedfrom each of the two or more pools of media content, the selected mediacontent having a range of tempos associated with the cadence, andwherein filling the bucket with media content from each of the two ormore pools of media content includes: filling the bucket with mediacontent from the first pool; determining whether a threshold amount ofmedia content from the first pool is reached; and when the thresholdamount of media content from the first pool is reached, filling thebucket with media content from the second pool.
 2. The system of claim1, wherein the two or more pools of media content include: a third poolhaving new media content that is from the filtered media content for therepetitive-motion activity and is not related to the media content inthe first pool.
 3. The system of claim 2, wherein media content from thetwo or more pools of media content is sorted into a plurality ofbuckets, each of the plurality of buckets representing a given tempo. 4.The system of claim 3, wherein each bucket increases in tempo by fivebeats per minute.
 5. The system of claim 4, wherein media content withineach bucket is ordered in importance.
 6. The system of claim 5, whereinmedia content from the first pool is played before media content fromthe second pool.
 7. The system of claim 1, wherein media content fromthe two or more pools of media content is sorted into a plurality ofbuckets, which each of the buckets representing a given tempo.
 8. Thesystem of claim 7, wherein media content within each bucket is orderedin importance.
 9. The system of claim 8, wherein media content from thefirst pool is played before media content from the second pool.
 10. Thesystem of claim 1, wherein the media content in the second pool isrelated to the media content in the first pool by sharing at least onecommon characteristic with the media content in the first pool.
 11. Thesystem of claim 10, wherein the at least one common characteristic is anartist of the media content.
 12. A media system, comprising: amedia-playback device including a media-output device that plays mediacontent items, the media-playback device configured to receive aselection of a desired tempo; and a server configured to: select mediacontent items for playback by the media-output device during arepetitive-motion activity by sorting a universe of media content intobuckets of tempos, and subsequently selecting the media content itemsfrom a bucket associated with a cadence of the repetitive-motionactivity, the bucket generated by: filtering media content according tothe repetitive-motion activity; placing each media content item from thefiltered media content into two or more pools of media content, the twoor more pools comprising: a first pool including media content that isfrom the filtered media content for the repetitive-motion activity andthat a user has previously listened to using the media system; and asecond pool including media content that is from the filtered mediacontent for the repetitive-motion activity, and that the user has notpreviously listened to using the media system and is related to themedia content in the first pool; and filling the bucket with mediacontent selected from each of the two or more pools of media content,the selected media content having a range of tempos associated with thecadence, and wherein filling the bucket with media content from each ofthe two or more pools of media content includes: filling the bucket withmedia content from the first pool; determining whether a thresholdamount of media content from the first pool is reached; and when thethreshold amount of media content from the first pool is reached,filling the bucket with media content from the second pool.
 13. Thesystem of claim 12, wherein the two or more pools of media contentinclude: a third pool having new media content that is from the filteredmedia content for the repetitive-motion activity and is not related tothe media content in the first pool.
 14. The system of claim 13, whereinmedia content from the two or more pools of media content is sorted intoa plurality of buckets, each of the plurality of buckets representing agiven tempo.
 15. The system of claim 14, wherein each bucket increasesin tempo by five beats per minute.
 16. The system of claim 15, whereinmedia content within each bucket is ordered in importance.
 17. Thesystem of claim 16, wherein media content from the first pool is playedbefore media content from the second pool.
 18. A method for selectingmedia content based upon tempo, the method comprising: selecting mediacontent items for playback during a repetitive-motion activity bysorting a universe of media content into buckets of tempos, andsubsequently selecting the media content items from a bucket associatedwith a cadence of the repetitive-motion activity, the bucket generatedby: filtering media content according to the repetitive-motion activity;placing each media content item from the filtered media content into twoor more pools of media content, the two or more pools comprising: afirst pool including media content that is from the filtered mediacontent for the repetitive-motion activity and that a user haspreviously listened to using a media system; and a second pool includingmedia content that is from the filtered media content for therepetitive-motion activity, and that the user has not previouslylistened to using the media system and is related to the media contentin the first pool; and filling the bucket with media content selectedfrom each of the two or more pools of media content, the selected mediacontent having a range of tempos associated with the cadence, andwherein filling the bucket with media content from each of the two ormore pools of media content includes: filling the bucket with mediacontent from the first pool; determining whether a threshold amount ofmedia content from the first pool is reached; and when the thresholdamount of media content from the first pool is reached, filling thebucket with media content from the second pool.
 19. The method of claim18, further comprising: creating a third pool having new media contentthat is from the filtered media content for the repetitive-motionactivity and is not related to the media content in the first pool. 20.The method of claim 19, wherein media content from the two or more poolsof media content is sorted into a plurality of buckets, each of theplurality of buckets representing a given tempo.
 21. The method of claim20, further comprising ordering media content within each bucket byimportance.
 22. The method of claim 21, further comprising playing mediacontent from the first pool before media content from the second pool.